Texture Analysis Based on Micro Primitive Descriptor (MPD)

Texture Analysis Based on Micro Primitive Descriptor (MPD)

Автор: Rasigiri Venkata lakshmi, E. Srinivasa Reddy, K. Chandra Sekharaiah

Журнал: International Journal of Modern Education and Computer Science (IJMECS) @ijmecs

Статья в выпуске: 2 vol.7, 2015 года.

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Texture classification is an important application in all the fields of image processing and computer vision. This paper proposes a simple and powerful feature set for texture classification, namely micro primitive descriptor (MPD). The MPD is derived from the 2×2 grid of a motif transformed image. The original image is divided into 2×2 pixel grids. Each 2×2 grid is replaced by a motif shape that minimizes the local ascent while traversing the 2×2 grid forming a motif transformed image. The proposed feature set extracts textural information of an image with a more detailed respect of texture characteristics. The results demonstrate that it is much more efficient and effective than representative feature descriptors, such as Random Threshold Vector Technique (RTV) features and Wavelet Transforms Based on Gaussian Markov Random Field (WTBGMF) approach for texture classification.

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Micro primitive descriptor, motif transformed image, texture classification

Короткий адрес: https://sciup.org/15014730

IDR: 15014730

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